Overview

Dataset statistics

Number of variables30
Number of observations898
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory280.8 KiB
Average record size in memory320.1 B

Variable types

Numeric7
DateTime1
Categorical22

Alerts

StandardFlag has constant value "0" Constant
PartNumber has constant value "SHC-5204_Sizing_20220411_KK.stp" Constant
Criteria01FreqMin has constant value "11860.6767578125" Constant
Criteria01FreqMax has constant value "12287.5" Constant
Criteria01AmplMin has constant value "0.0007921118522062898" Constant
Criteria01AmplMax has constant value "0.23011526465415955" Constant
Criteria02FreqMin has constant value "25226.84765625" Constant
Criteria02FreqMax has constant value "26043.6484375" Constant
Criteria02AmplMin has constant value "3.216741970391013e-05" Constant
Criteria02AmplMax has constant value "0.023195616900920868" Constant
Criteria03FreqMin has constant value "30068.68359375" Constant
Criteria03FreqMax has constant value "30914.974609375" Constant
Criteria03AmplMin has constant value "6.577152817044407e-06" Constant
Criteria03AmplMax has constant value "0.028094327077269554" Constant
Criteria04FreqMin has constant value "0" Constant
Criteria04FreqMax has constant value "0" Constant
Criteria04FreqActual has constant value "0" Constant
Criteria04AmplMin has constant value "0" Constant
Criteria04AmplMax has constant value "0" Constant
Criteria04AmplActual has constant value "0" Constant
Results_ID is highly correlated with Criteria01FreqActual and 2 other fieldsHigh correlation
Criteria01FreqActual is highly correlated with Results_ID and 3 other fieldsHigh correlation
Criteria01AmplActual is highly correlated with Criteria02AmplActualHigh correlation
Criteria02FreqActual is highly correlated with Results_ID and 3 other fieldsHigh correlation
Criteria02AmplActual is highly correlated with Criteria01AmplActual and 1 other fieldsHigh correlation
Criteria03FreqActual is highly correlated with Criteria01FreqActual and 1 other fieldsHigh correlation
Criteria03AmplActual is highly correlated with Criteria02AmplActualHigh correlation
Ratio is highly correlated with Results_ID and 2 other fieldsHigh correlation
Results_ID is highly correlated with Criteria01FreqActual and 2 other fieldsHigh correlation
Criteria01FreqActual is highly correlated with Results_ID and 3 other fieldsHigh correlation
Criteria01AmplActual is highly correlated with Criteria02AmplActualHigh correlation
Criteria02FreqActual is highly correlated with Results_ID and 2 other fieldsHigh correlation
Criteria02AmplActual is highly correlated with Criteria01AmplActual and 1 other fieldsHigh correlation
Criteria03FreqActual is highly correlated with Criteria01FreqActual and 1 other fieldsHigh correlation
Criteria03AmplActual is highly correlated with Criteria02AmplActualHigh correlation
Ratio is highly correlated with Results_ID and 1 other fieldsHigh correlation
Results_ID is highly correlated with Criteria01FreqActual and 1 other fieldsHigh correlation
Criteria01FreqActual is highly correlated with Results_ID and 2 other fieldsHigh correlation
Criteria02FreqActual is highly correlated with Criteria01FreqActual and 1 other fieldsHigh correlation
Criteria03FreqActual is highly correlated with Criteria02FreqActualHigh correlation
Ratio is highly correlated with Results_ID and 1 other fieldsHigh correlation
Criteria01FreqMax is highly correlated with Criteria04FreqMax and 20 other fieldsHigh correlation
Criteria04FreqMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria02FreqMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria03FreqMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria03FreqMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Result is highly correlated with Criteria01FreqMax and 19 other fieldsHigh correlation
Criteria02AmplMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria04AmplMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
PartNumber is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
StandardFlag is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria04FreqMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria04AmplActual is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria01FreqMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria02FreqMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria01AmplMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria03AmplMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria02AmplMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Ratio is highly correlated with Criteria01FreqMax and 19 other fieldsHigh correlation
Criteria04AmplMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria03AmplMin is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria01AmplMax is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Criteria04FreqActual is highly correlated with Criteria01FreqMax and 20 other fieldsHigh correlation
Results_ID is highly correlated with Result and 5 other fieldsHigh correlation
Result is highly correlated with Results_ID and 2 other fieldsHigh correlation
Criteria01FreqActual is highly correlated with Results_ID and 4 other fieldsHigh correlation
Criteria01AmplActual is highly correlated with Results_ID and 4 other fieldsHigh correlation
Criteria02FreqActual is highly correlated with Results_ID and 5 other fieldsHigh correlation
Criteria02AmplActual is highly correlated with Criteria01AmplActualHigh correlation
Criteria03FreqActual is highly correlated with Results_ID and 3 other fieldsHigh correlation
Criteria03AmplActual is highly correlated with Criteria01AmplActualHigh correlation
Ratio is highly correlated with Results_ID and 4 other fieldsHigh correlation
Results_ID is uniformly distributed Uniform
Ratio is uniformly distributed Uniform
Results_ID has unique values Unique
TestedAt has unique values Unique
Criteria01AmplActual has unique values Unique
Criteria02AmplActual has unique values Unique
Criteria03AmplActual has unique values Unique

Reproduction

Analysis started2022-07-30 07:38:19.635076
Analysis finished2022-07-30 07:38:39.235095
Duration19.6 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Results_ID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61379.5
Minimum60931
Maximum61828
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:39.374482image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum60931
5-th percentile60975.85
Q161155.25
median61379.5
Q361603.75
95-th percentile61783.15
Maximum61828
Range897
Interquartile range (IQR)448.5

Descriptive statistics

Standard deviation259.3745683
Coefficient of variation (CV)0.004225752381
Kurtosis-1.2
Mean61379.5
Median Absolute Deviation (MAD)224.5
Skewness0
Sum55118791
Variance67275.16667
MonotonicityStrictly increasing
2022-07-30T15:38:39.517660image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
614391
 
0.1%
616641
 
0.1%
617461
 
0.1%
617451
 
0.1%
617441
 
0.1%
617431
 
0.1%
617421
 
0.1%
617411
 
0.1%
617401
 
0.1%
617391
 
0.1%
Other values (888)888
98.9%
ValueCountFrequency (%)
609311
0.1%
609321
0.1%
609331
0.1%
609341
0.1%
609351
0.1%
609361
0.1%
609371
0.1%
609381
0.1%
609391
0.1%
609401
0.1%
ValueCountFrequency (%)
618281
0.1%
618271
0.1%
618261
0.1%
618251
0.1%
618241
0.1%
618231
0.1%
618221
0.1%
618211
0.1%
618201
0.1%
618191
0.1%

TestedAt
Date

UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.1 KiB
Minimum2022-07-29 09:43:39
Maximum2022-07-29 13:54:16
2022-07-30T15:38:39.675278image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:39.825876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

StandardFlag
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:39.982919image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:40.047744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

PartNumber
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size77.3 KiB
SHC-5204_Sizing_20220411_KK.stp
898 

Length

Max length31
Median length31
Mean length31
Min length31

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHC-5204_Sizing_20220411_KK.stp
2nd rowSHC-5204_Sizing_20220411_KK.stp
3rd rowSHC-5204_Sizing_20220411_KK.stp
4th rowSHC-5204_Sizing_20220411_KK.stp
5th rowSHC-5204_Sizing_20220411_KK.stp

Common Values

ValueCountFrequency (%)
SHC-5204_Sizing_20220411_KK.stp898
100.0%

Length

2022-07-30T15:38:40.112571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:40.176400image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
shc-5204_sizing_20220411_kk.stp898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Result
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
1
609 
0
289 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1609
67.8%
0289
32.2%

Length

2022-07-30T15:38:40.243345image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:40.404379image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
1609
67.8%
0289
32.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria01FreqMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size64.1 KiB
11860.6767578125
898 

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11860.6767578125
2nd row11860.6767578125
3rd row11860.6767578125
4th row11860.6767578125
5th row11860.6767578125

Common Values

ValueCountFrequency (%)
11860.6767578125898
100.0%

Length

2022-07-30T15:38:40.474364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:40.548205image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
11860.6767578125898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria01FreqMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.2 KiB
12287.5
898 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row12287.5
2nd row12287.5
3rd row12287.5
4th row12287.5
5th row12287.5

Common Values

ValueCountFrequency (%)
12287.5898
100.0%

Length

2022-07-30T15:38:40.612079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:40.674909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
12287.5898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria01FreqActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct30
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12142.71297
Minimum11578.125
Maximum12585.9375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:40.745682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11578.125
5-th percentile11882.8125
Q112046.875
median12187.5
Q312281.25
95-th percentile12328.125
Maximum12585.9375
Range1007.8125
Interquartile range (IQR)234.375

Descriptive statistics

Standard deviation149.4678113
Coefficient of variation (CV)0.01230926002
Kurtosis-0.688805441
Mean12142.71297
Median Absolute Deviation (MAD)117.1875
Skewness-0.3969332128
Sum10904156.25
Variance22340.62662
MonotonicityNot monotonic
2022-07-30T15:38:40.876369image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12304.687591
 
10.1%
12187.579
 
8.8%
12210.937577
 
8.6%
12281.2561
 
6.8%
12328.12559
 
6.6%
12070.312554
 
6.0%
11929.687553
 
5.9%
11882.812544
 
4.9%
11906.2544
 
4.9%
12257.812543
 
4.8%
Other values (20)293
32.6%
ValueCountFrequency (%)
11578.1251
 
0.1%
11765.6251
 
0.1%
11789.06251
 
0.1%
11812.51
 
0.1%
11835.93751
 
0.1%
11859.3757
 
0.8%
11882.812544
4.9%
11906.2544
4.9%
11929.687553
5.9%
11953.12524
2.7%
ValueCountFrequency (%)
12585.93753
 
0.3%
12445.31251
 
0.1%
12398.43751
 
0.1%
12351.562518
 
2.0%
12328.12559
6.6%
12304.687591
10.1%
12281.2561
6.8%
12257.812543
4.8%
12234.37531
 
3.5%
12210.937577
8.6%

Criteria01AmplMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
0.0007921118522062898
898 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0007921118522062898
2nd row0.0007921118522062898
3rd row0.0007921118522062898
4th row0.0007921118522062898
5th row0.0007921118522062898

Common Values

ValueCountFrequency (%)
0.0007921118522062898898
100.0%

Length

2022-07-30T15:38:40.993602image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:41.056434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0007921118522062898898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria01AmplMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size66.8 KiB
0.23011526465415955
898 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.23011526465415955
2nd row0.23011526465415955
3rd row0.23011526465415955
4th row0.23011526465415955
5th row0.23011526465415955

Common Values

ValueCountFrequency (%)
0.23011526465415955898
100.0%

Length

2022-07-30T15:38:41.121261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:41.185126image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.23011526465415955898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria01AmplActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.003892178897
Minimum0.0009452541126
Maximum0.009014188312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:41.270897image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0.0009452541126
5-th percentile0.001665784518
Q10.002774290566
median0.003754002624
Q30.004900543834
95-th percentile0.006456388463
Maximum0.009014188312
Range0.008068934199
Interquartile range (IQR)0.002126253268

Descriptive statistics

Standard deviation0.001493652364
Coefficient of variation (CV)0.3837573768
Kurtosis-0.3409558518
Mean0.003892178897
Median Absolute Deviation (MAD)0.001073596999
Skewness0.3921526342
Sum3.49517665
Variance2.230997384 × 10-6
MonotonicityNot monotonic
2022-07-30T15:38:41.420460image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0015716517811
 
0.1%
0.0030384641141
 
0.1%
0.0049269823361
 
0.1%
0.0063987369651
 
0.1%
0.0030772916511
 
0.1%
0.0034816386181
 
0.1%
0.002428778681
 
0.1%
0.006975033321
 
0.1%
0.003267989961
 
0.1%
0.0043844832111
 
0.1%
Other values (888)888
98.9%
ValueCountFrequency (%)
0.00094525411261
0.1%
0.0010127171411
0.1%
0.0010161736281
0.1%
0.0010860775361
0.1%
0.0011164542521
0.1%
0.0011194251711
0.1%
0.0011275450231
0.1%
0.0011588350171
0.1%
0.0012142924821
0.1%
0.0012702378441
0.1%
ValueCountFrequency (%)
0.0090141883121
0.1%
0.0086540663611
0.1%
0.0080096572641
0.1%
0.0079923747111
0.1%
0.0079467725011
0.1%
0.0078123514541
0.1%
0.0078072482721
0.1%
0.0078038843351
0.1%
0.0077217272481
0.1%
0.0076832897031
0.1%

Criteria02FreqMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
25226.84765625
898 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25226.84765625
2nd row25226.84765625
3rd row25226.84765625
4th row25226.84765625
5th row25226.84765625

Common Values

ValueCountFrequency (%)
25226.84765625898
100.0%

Length

2022-07-30T15:38:41.562122image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:41.625373image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
25226.84765625898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria02FreqMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size61.5 KiB
26043.6484375
898 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26043.6484375
2nd row26043.6484375
3rd row26043.6484375
4th row26043.6484375
5th row26043.6484375

Common Values

ValueCountFrequency (%)
26043.6484375898
100.0%

Length

2022-07-30T15:38:41.689683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:41.759498image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
26043.6484375898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria02FreqActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct63
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25673.69328
Minimum24843.75
Maximum26343.75
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:41.840311image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum24843.75
5-th percentile25101.5625
Q125453.125
median25781.25
Q325875
95-th percentile26039.0625
Maximum26343.75
Range1500
Interquartile range (IQR)421.875

Descriptive statistics

Standard deviation299.6434705
Coefficient of variation (CV)0.01167122577
Kurtosis-0.4035388126
Mean25673.69328
Median Absolute Deviation (MAD)164.0625
Skewness-0.6340660655
Sum23054976.56
Variance89786.20939
MonotonicityNot monotonic
2022-07-30T15:38:41.987231image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25851.562566
 
7.3%
25804.687559
 
6.6%
25828.12557
 
6.3%
25781.2546
 
5.1%
2587543
 
4.8%
25757.812538
 
4.2%
25945.312530
 
3.3%
25898.437527
 
3.0%
25992.187524
 
2.7%
25265.62524
 
2.7%
Other values (53)484
53.9%
ValueCountFrequency (%)
24843.752
 
0.2%
24890.6252
 
0.2%
24914.06253
 
0.3%
24937.53
 
0.3%
24960.93751
 
0.1%
24984.3755
 
0.6%
25007.81256
0.7%
25031.2514
1.6%
25054.68754
 
0.4%
25078.1254
 
0.4%
ValueCountFrequency (%)
26343.751
 
0.1%
26320.31253
 
0.3%
26296.8752
 
0.2%
26273.43753
 
0.3%
262501
 
0.1%
26226.56251
 
0.1%
26179.68751
 
0.1%
26156.253
 
0.3%
26132.81253
 
0.3%
26109.3759
1.0%

Criteria02AmplMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
3.216741970391013e-05
898 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.216741970391013e-05
2nd row3.216741970391013e-05
3rd row3.216741970391013e-05
4th row3.216741970391013e-05
5th row3.216741970391013e-05

Common Values

ValueCountFrequency (%)
3.216741970391013e-05898
100.0%

Length

2022-07-30T15:38:42.120758image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:42.184590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.216741970391013e-05898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria02AmplMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.7 KiB
0.023195616900920868
898 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.023195616900920868
2nd row0.023195616900920868
3rd row0.023195616900920868
4th row0.023195616900920868
5th row0.023195616900920868

Common Values

ValueCountFrequency (%)
0.023195616900920868898
100.0%

Length

2022-07-30T15:38:42.262382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:42.326211image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.023195616900920868898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria02AmplActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001670078981
Minimum2.157360541 × 10-5
Maximum0.0008299884503
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:42.414973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.157360541 × 10-5
5-th percentile3.905198755 × 10-5
Q18.451467147 × 10-5
median0.000142388446
Q30.0002199630944
95-th percentile0.0003738910396
Maximum0.0008299884503
Range0.0008084148449
Interquartile range (IQR)0.000135448423

Descriptive statistics

Standard deviation0.0001092552155
Coefficient of variation (CV)0.654191908
Kurtosis2.820322412
Mean0.0001670078981
Median Absolute Deviation (MAD)6.369693438 × 10-5
Skewness1.360258739
Sum0.1499730925
Variance1.193670211 × 10-8
MonotonicityNot monotonic
2022-07-30T15:38:42.665304image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00011062619161
 
0.1%
0.00011444453411
 
0.1%
0.00015180448831
 
0.1%
0.0001157180451
 
0.1%
0.00017993671641
 
0.1%
5.327793042 × 10-51
 
0.1%
0.00016181651151
 
0.1%
0.00034747394971
 
0.1%
9.855059761 × 10-51
 
0.1%
0.0001099942311
 
0.1%
Other values (888)888
98.9%
ValueCountFrequency (%)
2.157360541 × 10-51
0.1%
2.171119377 × 10-51
0.1%
2.239411879 × 10-51
0.1%
2.302634857 × 10-51
0.1%
2.424285594 × 10-51
0.1%
2.453543311 × 10-51
0.1%
2.484684046 × 10-51
0.1%
2.72059533 × 10-51
0.1%
2.792200212 × 10-51
0.1%
2.8459237 × 10-51
0.1%
ValueCountFrequency (%)
0.00082998845031
0.1%
0.00072081713011
0.1%
0.00057503447171
0.1%
0.00057467259471
0.1%
0.00056700635471
0.1%
0.00054504297441
0.1%
0.00053757184651
0.1%
0.00053665757881
0.1%
0.0005363661331
0.1%
0.0005318784971
0.1%

Criteria03FreqMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size62.4 KiB
30068.68359375
898 

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30068.68359375
2nd row30068.68359375
3rd row30068.68359375
4th row30068.68359375
5th row30068.68359375

Common Values

ValueCountFrequency (%)
30068.68359375898
100.0%

Length

2022-07-30T15:38:42.811911image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:42.898752image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
30068.68359375898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria03FreqMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size63.3 KiB
30914.974609375
898 

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row30914.974609375
2nd row30914.974609375
3rd row30914.974609375
4th row30914.974609375
5th row30914.974609375

Common Values

ValueCountFrequency (%)
30914.974609375898
100.0%

Length

2022-07-30T15:38:42.975146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:43.061631image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
30914.974609375898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria03FreqActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct44
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30442.38934
Minimum29789.0625
Maximum30914.0625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:43.153913image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum29789.0625
5-th percentile30046.875
Q130304.6875
median30445.3125
Q330609.375
95-th percentile30796.875
Maximum30914.0625
Range1125
Interquartile range (IQR)304.6875

Descriptive statistics

Standard deviation228.9961151
Coefficient of variation (CV)0.007522277984
Kurtosis-0.7075502625
Mean30442.38934
Median Absolute Deviation (MAD)164.0625
Skewness-0.2125367927
Sum27337265.62
Variance52439.22073
MonotonicityNot monotonic
2022-07-30T15:38:43.314072image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
30398.437554
 
6.0%
30445.312543
 
4.8%
30515.62540
 
4.5%
30421.87540
 
4.5%
30562.536
 
4.0%
30351.562536
 
4.0%
30539.062534
 
3.8%
3037533
 
3.7%
30609.37531
 
3.5%
30468.7529
 
3.2%
Other values (34)522
58.1%
ValueCountFrequency (%)
29789.06251
 
0.1%
29929.68752
 
0.2%
29953.1255
 
0.6%
29976.56254
 
0.4%
3000011
1.2%
30023.437514
1.6%
30046.87519
2.1%
30070.312518
2.0%
30093.7527
3.0%
30117.187520
2.2%
ValueCountFrequency (%)
30914.06251
 
0.1%
30890.6253
 
0.3%
30867.18757
 
0.8%
30843.755
 
0.6%
30820.312519
2.1%
30796.87522
2.4%
30773.437523
2.6%
3075027
3.0%
30726.562522
2.4%
30703.12519
2.1%

Criteria03AmplMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size68.5 KiB
6.577152817044407e-06
898 

Length

Max length21
Median length21
Mean length21
Min length21

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.577152817044407e-06
2nd row6.577152817044407e-06
3rd row6.577152817044407e-06
4th row6.577152817044407e-06
5th row6.577152817044407e-06

Common Values

ValueCountFrequency (%)
6.577152817044407e-06898
100.0%

Length

2022-07-30T15:38:43.447605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:43.525923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
6.577152817044407e-06898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria03AmplMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.7 KiB
0.028094327077269554
898 

Length

Max length20
Median length20
Mean length20
Min length20

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.028094327077269554
2nd row0.028094327077269554
3rd row0.028094327077269554
4th row0.028094327077269554
5th row0.028094327077269554

Common Values

ValueCountFrequency (%)
0.028094327077269554898
100.0%

Length

2022-07-30T15:38:43.593964image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:43.658967image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.028094327077269554898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria03AmplActual
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct898
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0001103152891
Minimum2.061684245 × 10-5
Maximum0.0002115173993
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.1 KiB
2022-07-30T15:38:43.746377image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2.061684245 × 10-5
5-th percentile5.699948742 × 10-5
Q18.86674643 × 10-5
median0.000110422694
Q30.0001341767638
95-th percentile0.0001612724031
Maximum0.0002115173993
Range0.0001909005568
Interquartile range (IQR)4.550929953 × 10-5

Descriptive statistics

Standard deviation3.260164692 × 10-5
Coefficient of variation (CV)0.2955315368
Kurtosis-0.1597768341
Mean0.0001103152891
Median Absolute Deviation (MAD)2.241828042 × 10-5
Skewness-0.06936782183
Sum0.09906312961
Variance1.062867382 × 10-9
MonotonicityNot monotonic
2022-07-30T15:38:43.897299image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0001292226371
 
0.1%
0.00013887016391
 
0.1%
0.00015537509171
 
0.1%
7.552164607 × 10-51
 
0.1%
7.708460907 × 10-51
 
0.1%
3.413145532 × 10-51
 
0.1%
7.875310985 × 10-51
 
0.1%
0.00010450209811
 
0.1%
9.186488751 × 10-51
 
0.1%
0.00010783887411
 
0.1%
Other values (888)888
98.9%
ValueCountFrequency (%)
2.061684245 × 10-51
0.1%
2.24270334 × 10-51
0.1%
2.366753324 × 10-51
0.1%
2.588135249 × 10-51
0.1%
2.698749086 × 10-51
0.1%
2.958806363 × 10-51
0.1%
3.023404861 × 10-51
0.1%
3.044886034 × 10-51
0.1%
3.251525777 × 10-51
0.1%
3.391093196 × 10-51
0.1%
ValueCountFrequency (%)
0.00021151739931
0.1%
0.00020545885491
0.1%
0.00019315759711
0.1%
0.00019234171491
0.1%
0.00019202831031
0.1%
0.00018903703311
0.1%
0.00018801935951
0.1%
0.00018740237281
0.1%
0.0001837070741
0.1%
0.00018233747691
0.1%

Criteria04FreqMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.035045image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.097965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria04FreqMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.162212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.229430image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria04FreqActual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.301368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.369186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria04AmplMin
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.438002image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.500834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria04AmplMax
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.566669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.633502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Criteria04AmplActual
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size51.0 KiB
0
898 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0898
100.0%

Length

2022-07-30T15:38:44.709305image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:44.779993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0898
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Ratio
Categorical

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIFORM

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size52.0 KiB
10
180 
30
180 
50
180 
20
180 
100
178 

Length

Max length3
Median length2
Mean length2.198218263
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row10

Common Values

ValueCountFrequency (%)
10180
20.0%
30180
20.0%
50180
20.0%
20180
20.0%
100178
19.8%

Length

2022-07-30T15:38:44.864729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-07-30T15:38:45.051229image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
20180
20.0%
50180
20.0%
30180
20.0%
10180
20.0%
100178
19.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2022-07-30T15:38:36.586915image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:28.790434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.034091image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:31.167965image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:32.739998image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:34.089705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.377519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:36.735559image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.069403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.168992image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:31.318023image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:32.924848image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:34.303979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.516146image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:36.888235image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.256298image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.311986image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:31.494661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:33.081923image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:34.462554image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.705642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:37.050977image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.416325image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.471280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:31.807817image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:33.245451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:34.628561image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.870201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:37.218871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.571944image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.628989image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:32.160905image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:33.414997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:34.831980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:36.029814image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:37.389966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.730922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:30.789983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:32.385306image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:33.583546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.048439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:36.183363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:37.542935image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:29.883818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:31.018029image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:32.543435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:33.820912image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:35.202985image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-07-30T15:38:36.428332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-07-30T15:38:45.198837image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-07-30T15:38:45.663968image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-07-30T15:38:46.145732image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-07-30T15:38:46.545326image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-07-30T15:38:46.848118image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-07-30T15:38:37.923275image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-07-30T15:38:38.987941image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

Results_IDTestedAtStandardFlagPartNumberResultCriteria01FreqMinCriteria01FreqMaxCriteria01FreqActualCriteria01AmplMinCriteria01AmplMaxCriteria01AmplActualCriteria02FreqMinCriteria02FreqMaxCriteria02FreqActualCriteria02AmplMinCriteria02AmplMaxCriteria02AmplActualCriteria03FreqMinCriteria03FreqMaxCriteria03FreqActualCriteria03AmplMinCriteria03AmplMaxCriteria03AmplActualCriteria04FreqMinCriteria04FreqMaxCriteria04FreqActualCriteria04AmplMinCriteria04AmplMaxCriteria04AmplActualRatio
0609312022-07-29 09:43:390SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512093.75000.0007920.2301150.00650825226.84765626043.64843825664.06250.0000320.0231960.00013930068.68359430914.97460930492.18750.0000070.0280940.00006300000010
1609322022-07-29 09:43:530SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512093.75000.0007920.2301150.00509425226.84765626043.64843825640.62500.0000320.0231960.00018630068.68359430914.97460930515.62500.0000070.0280940.00011600000010
2609332022-07-29 09:44:500SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512070.31250.0007920.2301150.00403225226.84765626043.64843825359.37500.0000320.0231960.00004430068.68359430914.97460930445.31250.0000070.0280940.00009400000010
3609342022-07-29 09:45:050SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512070.31250.0007920.2301150.00433425226.84765626043.64843825523.43750.0000320.0231960.00010930068.68359430914.97460930398.43750.0000070.0280940.00010200000010
4609352022-07-29 09:45:190SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512046.87500.0007920.2301150.00499825226.84765626043.64843825453.12500.0000320.0231960.00011130068.68359430914.97460930351.56250.0000070.0280940.00013800000010
5609362022-07-29 09:45:310SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512046.87500.0007920.2301150.00433625226.84765626043.64843825476.56250.0000320.0231960.00014130068.68359430914.97460930375.00000.0000070.0280940.00008600000010
6609372022-07-29 09:46:080SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512023.43750.0007920.2301150.00369025226.84765626043.64843825429.68750.0000320.0231960.00010130068.68359430914.97460930304.68750.0000070.0280940.00009400000010
7609382022-07-29 09:46:190SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512000.00000.0007920.2301150.00387625226.84765626043.64843825406.25000.0000320.0231960.00015030068.68359430914.97460930304.68750.0000070.0280940.00006700000010
8609392022-07-29 09:46:420SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512046.87500.0007920.2301150.00359725226.84765626043.64843825453.12500.0000320.0231960.00007730068.68359430914.97460930351.56250.0000070.0280940.00011000000010
9609402022-07-29 09:46:550SHC-5204_Sizing_20220411_KK.stp111860.67675812287.512046.87500.0007920.2301150.00407625226.84765626043.64843825453.12500.0000320.0231960.00009930068.68359430914.97460930375.00000.0000070.0280940.00009000000010

Last rows

Results_IDTestedAtStandardFlagPartNumberResultCriteria01FreqMinCriteria01FreqMaxCriteria01FreqActualCriteria01AmplMinCriteria01AmplMaxCriteria01AmplActualCriteria02FreqMinCriteria02FreqMaxCriteria02FreqActualCriteria02AmplMinCriteria02AmplMaxCriteria02AmplActualCriteria03FreqMinCriteria03FreqMaxCriteria03FreqActualCriteria03AmplMinCriteria03AmplMaxCriteria03AmplActualCriteria04FreqMinCriteria04FreqMaxCriteria04FreqActualCriteria04AmplMinCriteria04AmplMaxCriteria04AmplActualRatio
888618192022-07-29 13:52:530SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512304.68750.0007920.2301150.00617425226.84765626043.64843825828.12500.0000320.0231960.00019530068.68359430914.97460930375.00000.0000070.0280940.000139000000100
889618202022-07-29 13:52:570SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512304.68750.0007920.2301150.00521525226.84765626043.64843825828.12500.0000320.0231960.00016930068.68359430914.97460930351.56250.0000070.0280940.000155000000100
890618212022-07-29 13:53:040SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00395225226.84765626043.64843825875.00000.0000320.0231960.00019830068.68359430914.97460930398.43750.0000070.0280940.000123000000100
891618222022-07-29 13:53:060SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00662425226.84765626043.64843825898.43750.0000320.0231960.00029330068.68359430914.97460930445.31250.0000070.0280940.000153000000100
892618232022-07-29 13:53:550SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00639925226.84765626043.64843825851.56250.0000320.0231960.00023430068.68359430914.97460930398.43750.0000070.0280940.000130000000100
893618242022-07-29 13:53:570SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512304.68750.0007920.2301150.00576425226.84765626043.64843825851.56250.0000320.0231960.00026230068.68359430914.97460930398.43750.0000070.0280940.000149000000100
894618252022-07-29 13:54:050SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00498525226.84765626043.64843825851.56250.0000320.0231960.00017630068.68359430914.97460930398.43750.0000070.0280940.000161000000100
895618262022-07-29 13:54:080SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00515225226.84765626043.64843825851.56250.0000320.0231960.00016930068.68359430914.97460930398.43750.0000070.0280940.000139000000100
896618272022-07-29 13:54:140SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00563825226.84765626043.64843825875.00000.0000320.0231960.00015730068.68359430914.97460930421.87500.0000070.0280940.000129000000100
897618282022-07-29 13:54:160SHC-5204_Sizing_20220411_KK.stp011860.67675812287.512328.12500.0007920.2301150.00439625226.84765626043.64843825875.00000.0000320.0231960.00015630068.68359430914.97460930421.87500.0000070.0280940.000114000000100